The present invention relates to product checkout devices and more specifically to a method of recognizing produce items using checkout frequency.
Bar code readers are well known for their usefulness in retail checkout and inventory control. Bar code readers are capable of identifying and recording most items during a typical transaction since most items are labeled with bar codes.
Items which are typically not identified and recorded by a bar code reader are produce items, since produce items are typically not labeled with bar codes. Bar code readers may include a scale for weighing produce items to assist in determining the price of such items. But identification of produce items is still a task for the checkout operator, who must identify a produce item and then manually enter an item identification code. Operator identification methods are slow and inefficient because they typically involve a visual comparison of a produce item with pictures of produce items, or a lookup of text in table. Operator identification methods are also prone to error, on the order of fifteen percent.
A produce recognition system is disclosed in the cited co-pending application. A produce item is placed over a window in a produce data collector, the produce item is illuminated, and the spectrum of the diffuse reflected light from the produce item is measured. A terminal compares the spectrum to reference spectra in a library. The terminal determines candidate produce items and corresponding confidence levels and chooses the candidate with the highest confidence level. The terminal may additionally display the candidates for operator verification and selection.
Different produce items usually have very different checkout frequencies. Therefore, it would be desirable to supplement spectral data with checkout frequency information in order to improve the speed and accuracy of recognizing produce items.
In accordance with the teachings of the present invention, a method of recognizing produce items using checkout frequency is provided.
A method is proposed to utilize the checkout frequency as an a priori probability in a produce recognition system. No particular statistical model is assumed in applying Bayes Rule to calculate an a posteriori probability, which is used to rank candidate identifications for the produce item. A defined DML algorithm can provide a readily available method for computing conditional probability densities.
The method includes the steps of collecting produce data from the produce item, determining DML values between the produce data and reference produce data for a plurality of types of produce items, determining conditional probability densities for all of the types of produce items using the DML values, combining the conditional probability densities together to form a combined conditional probability density, determining checkout frequencies for the produce types, determining probabilities for the types of produce items from the combined conditional probability density and the checkout frequencies, determining a number of candidate identifications from the probabilities, and identifying the produce item from the number candidate identifications.
It is accordingly an object of the present invention to provide a method of recognizing produce items using checkout frequency.
It is another object of the present invention to reduce the time involved in processing produce items.
It is another object of the present invention to provide a more accurate list of candidate produce items to a checkout operator.
It is another object of the present invention to provide a method of recognizing produce items using checkout frequency to supplement data captured from the produce items.